This disclosure relates to systems and methods for detecting defects in data storage media that store data such as for archiving and subsequent retrieval purposes.
With a proliferation of removable non-volatile data storage media on which increasing amounts of data can be recorded and/or rerecorded, there has arisen an incumbent need to be able to detect defects in such media in order to, for example, avoid data corruption and adverse effects to downstream units, devices and adaptations based on trying to process corrupt data. Such defects may manifest themselves in a number of ways. Some of the more common manifestations of the types of defects that may be advantageous to detect include, but are not limited to, excessive amplitude variations such as amplitude drops, amplitude jumps, and/or shifts in the signal with a drop in dynamic range. These defects may be caused by, for example, a record/playback head flying too high or too low over a data track, being offset from the track, or by collisions with particles or asperities on the surface of the media. The defects may be temporary such that they disappear at the next read operation or after the next write operation or they may grow in severity or extent with successive operations.
In order to be able to retrieve data with a certain desired confidence level, it is important that the integrity of such data be established. The existence of a defect on a data storage medium represents a situation that often cannot be easily modeled or otherwise modeled at all. Hence, in view of the adverse impact that a defect may have on the integrity of the read or retrieved data and components or adaptations that may attempt to process the data, a reliable defect detector system may prove advantageous.
FIG. 1 schematically illustrates a simplified conventional data signal communication and processing system 1000. As shown in FIG. 1, the system 1000 may include an input signal source 1010 and a number of components for processing the input signal. These components may include, for example, an encoder 1020, and one or more transmission channel filters 1040. In this manner, an input signal received via the input signal source 1010 may be processed by one or more of the above-mentioned devices in order to provide a substantially distortion-immune and bandwidth-efficient signal to be recorded on a recording or transmission channel 1050. Such recording or transmission channel 1050 in different embodiments, it should be appreciated, may comprise virtually any form of, for example, wire-line media, wireless media, or data storage medium.
The encoder 1020 may encode the input signal to, for example, improve a Bit Error Ratio (BER) of the signal.
A transmission channel filter 1040 may shape the input signal waveform to attempt to make optimal use of the available channel bandwidth to support a highest data storage density based on an optimally filtered signal, with minimal signal distortion, from sources of distortion such as inter-symbol interference (ISI).
In general, the encoder 1020 and transmission channel filter 1040 are referred to as the transmitting side of the system for receiving an input signal from an input signal source 1010 and optimally presenting such a signal to a recording or transmission channel 1050. In other words, the overall objective of the transmitter side elements 1020 and 1040, as depicted in FIG. 1, is to allow the data to be stored in the recording or transmission channel 1050, received in raw form from the input signal source 1010, to achieve a desired level of immunity from various sources of distortion, degradation and noise, as well as to be converted into a form such that the data has a desired level of reliability after transmission through the recording or transmission channel 1050, to include being stored on a data storage medium.
As shown in FIG. 1, receiver side processing is undertaken by a series of receiver side elements consisting in this exemplary embodiment of elements 1070, 1080, 1090 and 1110. Processing through these elements essentially reverses the processing that the transmitter side elements performed to render the output signal as close a match to the input signal as pre-determined by a specified fidelity criterion. The receiver signal elements may include an automatic gain controller 1070, a receiver channel filter 1080, an equalizer 1090 and a decoder 1110, each element included with an objective of delivering to an output signal sink 1120, an output signal that precisely matches the input signal received from the input signal source 1010.
The automatic gain controller 1070 may modify the level of the received signal or recovered data such that the data signal is within an appropriate dynamic range to proceed through further processing.
The receiver channel filter 1080 may process the signal in much the same manner as the transmitter channel filtering performed by the transmitter channel filter 1040.
The equalizer 1090 may filter the dynamic range adjusted signal, which was previously filtered by the receiver channel filter 1080, in an attempt to mitigate the impact of phenomena, such as, for example, ISI. As the equalizer 1090 may be provided to further optimize the output signal to a specific capability of the output signal sink 1120, any one of the several available equalizer algorithms including, but not limited to, a linear feedforward equalizer, a linear feedback equalizer or a decision feedback equalizer may be used.
The decoder 1110 may optimally decode the signal to correspond to the specific capabilities of the output signal sink 1120. There are several available decoding schemes any of which may be employed by the decoder 1110. These include, but are not limited to, threshold decoding, Viterbi decoding and/or Turbo decoding.
The integrity of the information stored on a data storage media or transmitted via a recording/transmission channel is of paramount importance. The integrity of the data retrieved may be impacted in a number of ways. For example, retrieved data may be corrupted due to random noise, bursty noise, inter-symbol interference (ISI), non-linear distortions in the channel such as non-linear transition shifts (NLTS), read-write offsets and writing non-idealities. Many of these degradations may be corrected by applying one or more of the above methods commonly used in high-speed communications links, such as signal processing techniques, coding and channel estimation, and subsequent corrections. However, other factors may contribute to degradation and distortion in recovered data, particularly data stored on various types of data storage media. Principal among these factors effecting degradation and/or distortion in recovered data may be defects in the data storage media itself. Physical, and/or recording equipment induced, defects in any data storage medium that stores an input information signal as stored data have the potential to severely impact the integrity of the retrieved data and to render ineffective, or unusable, components and adaptations that attempt to process the data corrupted by one or more defects.
Such defects are not conventionally accounted for because conventional input data signal processing methods and capabilities, such as those discussed above, do not lend themselves to such defect detection and/or mitigation. Data storage media are often considered virtually defect-free. Based on the amount of data being compacted onto individual data storage media today, such an assumption may not recognize the possible hazards to the recovery and/or reproduction of data stored on such media. Complicating this problem even further is the fact that a given defect could be only temporary, and/or otherwise non-repeatable. In such an instance the defect could disappear on the next write cycle or the media could deteriorate from cycle to cycle. It is for this reason that modeling of certain defects, unlike many other of the factors that may degrade the integrity of the input signal data which can be corrected or mediated through a series of devices and related processes such as those described above, i.e., be they noise or other channel related failures, is extremely difficult. Possible contributing factors to the defects could be a record/playback head flying too high or too low over a data track, being offset from the track, or by colliding with particles or asperities on the surface of the media.
Impacts of these defects at the read channel retrieved signal manifest themselves in many forms or combinations of phenomena. These manifestations include but are not limited to the following. First, the amplitude of the recorded data signal sometimes drops to an extent of signal wipeout over a duration of several bits. Second, the amplitude may jump, possibly due to thermal aperities, and third, the signal may shift, often with an accompanying dynamic range drop. The random nature of the types of defects described, and the fact that many and often widely varied factors may contribute to the defects make these anomalies very difficult to model with an objective of such modeling being to subsequently isolate or compensate for the defects.
Previous efforts to address the problems of defect detection have included methods for generating special patterns, or defect scan patterns, to detect anomalies and isolate local defects (e.g. MADS) in the magnetic flux coupling of a recording media to a read head. The defect scan patterns affect normal operation of magnetic data storage devices. Other methods of defect detection, such as atomic force microscopy (AFM) and other microprobe techniques, are suited to laboratory use but not necessarily to production use due to their limited throughput.
These efforts encompass a range of solutions from partial detection to defect isolation. Techniques include harmonic analysis of the read head signal to detect head-to-media spacing or flying height (e.g. Harmonica Sensor (HSC)). Harmonic analysis methods may implicitly use thermal, fluid dynamic, and electromagnetic factors to optimize the average height of the read head. These methods may preclude effective means to isolated defects because the defect waveform is convolved with a long impulse response due to the harmonic analysis averaging window. In other words, harmonic-sensing methods may impose narrow analysis bandwidths around the harmonics to be analyzed. Narrow analysis bandwidths impose slow response times that prevent pinpointing defects.
Additional conventional techniques include run time methods such as level detectors in an analog front end (AFE). Run time methods do not require special patterns or defect scans. Run time methods can pinpoint defects, such as amplitude jumps due to thermal asperities. However, level detection methods have not proven to be very sensitive to detecting level drops.
Shortfalls in prior efforts include: (1) incompatibility with run-time or real-time operations; (2) requirements for special patterns; (3) inability to localize or pinpoint defects; and/or (4) insensitivity to signal amplitude jumps, amplitude drops, and/or signal shifts accompanied by dynamic range shifts.